arXiv subjects

Tom Brown

Publications and source records attributed to Tom Brown.

At least 37 records · Page 2Linked to original sources

arXiv2024

WorkflowHub: a registry for computational workflows

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 700 workflows registered.

cs.DL↗

arXiv2024

H$_2$ and CO$_2$ Network Strategies for the European Energy System

Hydrogen and carbon dioxide transport can both play an essential role in climate-neutral energy systems. Hydrogen networks help serve regions with high energy demand, while excess emissions are transported away in carbon dioxide networks. For the synthesis of carbonaceous fuels, it is less clear which input should be transported: hydrogen to carbon point sources or carbon to low-cost hydrogen. We explore both networks' potential synergies and competition in a cost-optimal carbon-neutral European energy system. In a direct comparison, a hydrogen network is more cost-effective than a carbon network, as it serves to transport hydrogen to demand and to point source of carbon for utilization. However, in a hybrid scenario where both networks are present, the carbon network effectively complements the hydrogen network, promoting carbon capture from distributed biomass and reducing reliance on direct air capture. The layouts of the hydrogen and carbon dioxide networks are robust if the climate target is tightened to be net-negative.

physics.soc-ph↗

arXiv2024

Modelling the High-Voltage Grid Using Open Data for Europe and Beyond

This paper provides the background, methodology and validation for constructing a representation of the European high-voltage grid, including and above 200 kV, based on public data provided by OpenStreetMap. The model-independent grid dataset is published under the Open Data Commons Open Database (ODbL 1.0) licence and can be used for large-scale electricity as well as energy system modelling. The dataset and workflow are provided as part of PyPSA-Eur -- an open-source, sector-coupled optimisation model of the European energy system. By integrating with the codebase for initiatives such as PyPSA-Earth, the value of open and maintainable high-voltage grid data extends to the global context. By accessing the latest data through the the Overpass turbo API, the dataset can be easily reconstructed and updated within minutes. To assess the data quality, this paper further compares the dataset with official statistics and representative model runs using PyPSA-Eur based on different electricity grid representations.

physics.soc-ph↗

arXiv2024

The impact of temporal hydrogen regulation on hydrogen exporters and their domestic energy transition

As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. However, the large-scale installation of on-grid hydrogen electrolysis for export can have profound impacts on domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benets of domestic climate change mitigation and hydrogen exports, whereby exports can reduce domestic electricity prices while mitigation reduces hydrogen export prices. However, increasing hydrogen exports quickly in a system that is still dominated by fossil fuels can substantially raise domestic electricity prices, if green hydrogen production is not regulated. Surprisingly, temporal matching of hydrogen production lowers domestic electricity cost by up to 31% while the effect on exporters is minimal. This policy instrument can steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance among actors.

physics.soc-ph↗

arXiv2024

Offshore power and hydrogen networks for Europe's North Sea

The European North Sea has a vast renewable energy potential and can be a powerhouse for Europe's energy transition. However, currently there is uncertainty about how much offshore wind energy can be integrated, whether offshore grids should be meshed and to what extent offshore hydrogen should play a role. To address these questions, we use the open-source energy system optimization model PyPSA-Eur to model a European carbon-neutral sector-coupled energy system in high spatial and temporal resolution. We let the model endogenously decide how much offshore wind is deployed and which infrastructure is used to integrate the offshore wind. We find that with point-to-point connections like we have today, 310 GW offshore wind can be integrated in the North Sea. However, if we allow meshed networks and hydrogen, we find that this can be raised to 420 GW with cost savings up to 15 billion euros per year. Furthermore, we only observe significant amounts of up to 75 GW of floating wind turbines in the North Sea if we have offshore hydrogen production. Generally, the model opts for offshore wind integration through a mix of both electricity and hydrogen infrastructure. However, the bulk of the offshore energy is transported as hydrogen, which is twice as much as the amount transported as electricity. Moreover, we find that the offshore power network is mainly used for offshore wind integration, with only a small portion used for inter-country transmission.

physics.soc-ph↗

arXiv2024

Energy Imports and Infrastructure in a Carbon-Neutral European Energy System

Importing renewable energy to Europe offers many potential benefits, including reduced energy costs, lower pressure on infrastructure development, and less land-use within Europe. However, there remain many open questions: on the achievable cost reductions, how much should be imported, whether the energy vector should be electricity, hydrogen or hydrogen derivatives like ammonia or steel, and their impact on Europe's domestic energy infrastructure needs. This study integrates the TRACE global energy supply chain model with the sector-coupled energy system model for Europe PyPSA-Eur to explore scenarios with varying import volumes, costs, and vectors. We find system cost reductions of 1-14%, depending on assumed import costs, with diminishing returns for larger import volumes and a preference for methanol, steel and hydrogen imports. Keeping some domestic power-to-X production is beneficial for integrating variable renewables, utilising waste heat from fuel synthesis and leveraging local sustainable carbon sources. Our findings highlight the need for coordinating import strategies with infrastructure policy and reveal maneuvering space for incorporating non-cost decision factors.

physics.soc-ph↗

arXiv2024

Spatio-temporal load shifting for truly clean computing

Companies with datacenters are procuring significant amounts of renewable energy to reduce their carbon footprint. There is increasing interest in achieving 24/7 Carbon-Free Energy (CFE) matching in electricity usage, aiming to eliminate all carbon footprints associated with electricity consumption on an hourly basis. However, the variability of renewable energy resources poses significant challenges for achieving this goal. We explore the impact of shifting computing jobs and associated power loads both in time and between datacenter locations. We develop an optimization model to simulate a network of geographically distributed datacenters managed by a company leveraging spatio-temporal load flexibility to achieve 24/7 CFE matching. We isolate three signals relevant for informed use of load flexiblity: varying average quality of renewable energy resources, low correlation between wind power generation over long distances due to different weather conditions, and lags in solar radiation peak due to Earth's rotation. We illustrate that the location of datacenters and the time of year affect which signal drives an effective load-shaping strategy. The energy procurement and load-shifting decisions based on informed use of these signals facilitate the resource-efficiency and cost-effectiveness of clean computing -- the costs of 24/7 CFE are reduced by 1.29$\pm$0.07 EUR/MWh for every additional percentage of flexible load. We provide practical guidelines on how companies with datacenters can leverage spatio-temporal load flexibility for truly clean computing. Our results and the open-source optimization model can also be useful for a broader variety of companies with flexible loads and an interest in eliminating their carbon footprint.

physics.soc-ph↗

arXiv2023

On the means, costs, and system-level impacts of 24/7 carbon-free energy procurement

A growing number of public and private energy buyers are interested in 24/7 carbon-free energy (CFE) procurement, which means that every kilowatt-hour of electricity consumption is met by carbon-free sources at all times. It has the potential to overcome the limitations of established procurement schemes, such as the temporal mismatch between clean electricity supply and buyers' demand that is inherent to "volumetric" matching. Yet it is unclear how 24/7 CFE procurement affects the rest of the power system, and whether this effect is consistent across regional contexts and different levels of system cleanness. We use a mathematical model to systematically examine different designs, optimal procurement strategies, costs, and impacts of the 24/7 CFE matching, both for participating buyers and for regions where voluntary procurement occurs. We examine mechanisms driving system-level missions reduction and how they vary across regions and over time. Our results indicate that clean energy procurement commitments have consistent beneficial effects on participants and the electricity system. Even as grids become cleaner over time, the hourly matching strategy contributes significantly to system-level emissions reduction. In addition, voluntary commitments to 24/7 CFE have a further transformative effect on electricity systems through accelerated innovation and early deployment of advanced energy technologies.

physics.soc-ph↗

arXiv2023

Testing Robustness Against Unforeseen Adversaries

Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-world applications developers are unlikely to have access to the full range of attacks or corruptions their system will face. Furthermore, worst-case inputs are likely to be diverse and need not be constrained to the L_p ball. To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen new non-L_p attacks. To perform well on ImageNet-UA, defenses must overcome a generalization gap and be robust to a diverse attacks not encountered during training. In extensive experiments, we find that existing robustness measures do not capture unforeseen robustness, that standard robustness techniques are beat by alternative training strategies, and that novel methods can improve unforeseen robustness. We present ImageNet-UA as a useful tool for the community for improving the worst-case behavior of machine learning systems.

cs.LG↗

arXiv2023

Benefits from Islanding Green Hydrogen Production

In wind- and solar-dominated energy systems it has been assumed that there are synergies between producing electricity and electrolytic hydrogen since electrolysis can use excess electricity that would otherwise be curtailed. However, it remains unclear whether these synergies hold true at higher levels of hydrogen demand and how they compare with benefits of off-grid, islanded hydrogen production, such as better renewable resources and cost savings on electronics due to relaxed power quality standards. Using a mathematical model across two geographical locations for Germany, Spain, Australia, and Great Britain, we explore trade-offs and synergies between integrated and islanded electrolysers. Below a certain threshold, between 5% and 40% hydrogen share depending on the country, integrated electrolysers offer synergies in flexibility and reduced curtailment. Above these thresholds, islanded electrolysers become more favourable. Without cost advantages, systems including islanded electrolysers in Germany achieve up to 21% lower hydrogen costs than systems with only integrated electrolysers. With 25% island cost advantage, this benefit rises to 40% lower hydrogen costs. Our study identifies three investment regimes with country-specific transition points that vary based on island cost advantages and each country's renewable resources. Based on our results we provide guidelines for countries considering how to deploy electrolysers.

physics.soc-ph↗

arXiv2023

Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?

Spatially highly-resolved capacity expansion models are often simplified to a lower spatial resolution because they are computationally intensive. The simplification mixes sites with different renewable features while ignoring transmission lines that can cause congestion. As a consequence, the results may represent an infeasible system when the capacities are fed back at higher spatial detail. Thus far there has been no detailed investigation of how to disaggregate results and whether the spatially highly-resolved disaggregated model is feasible. This is challenging since there is no unique way to invert the clustering. This article is split into two parts to tackle these challenges. First, methods to disaggregate spatially low-resolved results are presented: (a) an uniform distribution of regional results across its original highly-resolved regions, (b) a re-optimisation for each region separately, (c) an approach that minimises the "excess electricity". Second, the resulting highly-resolved models' feasibility is investigated by running an operational dispatch. While re-optimising yields the best results, the third inverse method provides comparable results for less computational effort. Feasibility-wise, the study design strengthens that modelling countries by single regions is insufficient. State-of-the-art reduced models with 100-200 regions for Europe still yield 3%-7% of load-shedding, depending on model resolution and inverse method.

cs.CE↗

arXiv2023

Offshore Wind Integration in the North Sea: The Benefits of an Offshore Grid and Floating Wind

Wind energy has become increasingly important for meeting Europe's energy needs. While onshore wind expansion faces public acceptance problems, for offshore wind the European Commission has introduced ambitious goals to increase capacity from 15GW to 300GW in 2050. Incorporating more offshore wind electricity into the power grid may offer a more widely accepted way to satisfy Europe's energy demand. In particular, the North Sea region has large potential for offshore wind generation. However, to fully exploit the wind potential in the North Sea, the grid integration of offshore wind and floating wind turbines are vital, especially when onshore wind capacity and onshore grid expansion are constrained. For the grid integration, a meshed offshore grid can offer a viable alternative to the standard direct connection of offshore wind parks to the nearest point on land combined with point-to-point HVDC connections. In this paper, we investigate the benefits of having a meshed offshore grid in the North Sea and considering floating wind besides fixed-bottom wind installations. In our analysis, we look at eight different scenarios, where onshore wind potentials and onshore line expansion are limited, to explore the effects of low public acceptance. Our results demonstrate that the presence of an offshore grid can reduce total system costs by up to 2.6 bn Euro/a. In the scenarios with an offshore meshed grid, ~8% more offshore wind capacities are built compared to the scenarios without a meshed grid. Furthermore, the analysis shows that if onshore wind potentials are restricted, floating wind turbines play a key role and compensate for lacking onshore wind capacities.

physics.soc-ph↗

arXiv2023

The Potential Role of a Hydrogen Network in Europe

Electricity transmission expansion has suffered many delays in Europe in recent decades, despite its significance for integrating renewable electricity into the energy system. A hydrogen network which reuses the existing fossil gas network could not only help to supply demand for low-emission fuels, but could also to balance variations in wind and solar energy across the continent and thus avoid power grid expansion. We pursue this idea by varying the allowed expansion of electricity and hydrogen grids in net-zero CO2 scenarios for a sector-coupled and self-sufficient European energy system with high shares of renewables. We cover the electricity, buildings, transport, agriculture, and industry sectors across 181 regions and model every third hour of a year. With this high spatio-temporal resolution, the model can capture bottlenecks in transmission networks, the variability of demand and renewable supply, as well as regional opportunities for the retrofitting of legacy gas infrastructure and the development of geological hydrogen storage. Our results show consistent system cost reductions with a pan-continental hydrogen network that connects regions with low-cost and abundant renewable potentials to demand centres, synthetic fuel production and cavern storage sites. Developing a hydrogen network reduces system costs by up to 26 billion Euros per year (3.4%), with the highest benefits when electricity grid reinforcements cannot be realised. Between 64% and 69% of this network could be built from repurposed natural gas pipelines. However, we find that hydrogen networks can only partially substitute for power grid expansion. While the expansion of both networks together can achieve the largest cost savings of 10%, the expansion of neither is truly essential as long as higher costs can be accepted and regulatory changes are made to manage grid bottlenecks.

physics.soc-ph↗

arXiv2023

The Capacity for Moral Self-Correction in Large Language Models

We test the hypothesis that language models trained with reinforcement learning from human feedback (RLHF) have the capability to "morally self-correct" -- to avoid producing harmful outputs -- if instructed to do so. We find strong evidence in support of this hypothesis across three different experiments, each of which reveal different facets of moral self-correction. We find that the capability for moral self-correction emerges at 22B model parameters, and typically improves with increasing model size and RLHF training. We believe that at this level of scale, language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping, bias, and discrimination. As such, they can follow instructions to avoid certain kinds of morally harmful outputs. We believe our results are cause for cautious optimism regarding the ability to train language models to abide by ethical principles.

cs.CL↗

arXiv2023

Endogenous learning for green hydrogen in a sector-coupled energy model for Europe

Many studies have shown that hydrogen could play a large role in the energy transition for hard-to-electrify sectors, but previous modelling has not included the necessary features to assess its role. They have either left out important sectors of hydrogen demand, ignored the temporal variability in the system or neglected the dynamics of learning effects. We address these limitations and consider learning-by-doing for the full green hydrogen production chain with different climate targets in a detailed European sector-coupled model. Here, we show that in the next 10 years a faster scale-up of electrolysis and renewable capacities than envisaged by the EU in the REPowerEU Plan is cost-optimal in order to reach the +1.5°C target. This reduces the costs for hydrogen production to 1.26 Eur/kg by 2050. Hydrogen production switches from grey to green hydrogen, omitting the option of blue hydrogen. If electrolysis costs are modelled without dynamic learning-by-doing, then the electrolysis scale-up is significantly delayed, while total system costs are overestimated by up to 13% and the levelised cost of hydrogen is overestimated by 67%.

physics.soc-ph↗

arXiv2023

Import options for chemical energy carriers from renewable sources to Germany

Import and export of fossil energy carriers are cornerstones of energy systems world-wide. If energy systems are to become climate neutral and sustainable, fossil carriers need to be substituted with carbon neutral alternatives or electrified if possible. We investigate synthetic chemical energy carriers, H2, CH4, MeOH, NH3 and Fischer-Tropsch fuels (FTF), produced using electricity from RES as fossil substitutes. [...] We model the sourcing of feedstock chemicals, synthesis and transport along nine different Energy Supply Chains to Germany (DE) and compare import options for seven locations around the world against each other and with domestically sourced alternatives on the basis of their respective cost per unit of H2 and energy delivered. We find that for each type of chemical energy carrier, there is an import option with lower costs compared to domestic production in DE. No single exporting country or energy carrier has a unique cost advantage, since for each energy carrier and country there are cost-competitive alternatives. This allows exporter and infrastructure decisions to be made based on other criteria than energy and cost. The lowest cost means for importing of energy and H2 are by H2 pipeline from Denmark, Spain and Western Asia and Northern Africa starting at 36 EUR/MWhLHV to 42 EUR/MWh-LHV or 1.0 EUR/kg-H2 to 1.3 EUR/kg-H2 (in 2050, assuming 5 % p.a. capital cost). For complex energy carriers derived from H2 like CH4, NH3, MeOH or FTF, imports from Argentina by ship to DE are lower cost than closer exporters in the European Union or Western Asia and Northern Africa. For meeting H2 demand, direct H2 imports are more attractive than indirect routes using CH4, MeOH or NH3 imports and subsequent decomposition to H2 because of high capital investment costs and energetic losses. We make our model and data available under open licenses for adaptation and reuse.

physics.soc-ph↗

arXiv2023

The strong effect of network resolution on electricity system models with high shares of wind and solar

Energy system modellers typically choose a low spatial resolution for their models based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for wind and solar generation. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different wind and solar capacity factors in the same resource class tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe's power system with a high share of renewables, taking advantage of newly-available high-resolution datasets as well as computational advances. We vary the number of nodes, interpolating between a 37-node model based on country and synchronous zone boundaries, and a 1024-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 23% as generation has to be sourced more locally at sites with worse capacity factors. These effects are most pronounced in scenarios where grid expansion is limited, for example, by low local acceptance. We show that allowing grid expansion mitigates some of the effects of the low grid resolution, and lowers overall costs by around 16%.

physics.soc-ph↗

arXiv2022

Discovering Language Model Behaviors with Model-Written Evaluations

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

cs.CL↗